Abstract
The next generation Internet of Things (IoT) applications are offering multiple services and run in a distributed heterogeneous environment. In such applications, Quality of Service (QoS) requirements are in jeopardy when the computing operations are only outsourced to the public cloud. For IoT applications, a comprehensive framework that supports QoS-aware service placement in a fog computing environment is highly required. It is a challenging task to orchestrate the time critical IoT applications in the fog environment. To alleviate this problem, this paper proposes a novel multitier fog computing architecture called Deadline-oriented Service Placement (DoSP) that provides the services both in fog and cloud nodes. This research work proposed a methodology to utilize low-cost fog resources while ensuring that the response time satisfies a given time constraint. It uses the Genetic Algorithm (GA) to dynamically determine the service placement in the fog environment. In this work, we used the iFogSim simulator to model DoSP and measured the impact of the service placement technique in terms of service deadline. It has been observed that through the proposed solution, there is a reduction in service execution delay, i.e., approximately 10.19% of the overall response time to the EdgeWard and 2.58% to the Cloud Only.
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References
Kochovski P, Stankovski V (2018) Supporting smart construction with dependable edge computing infrastructures and applications. Autom Constr 85:182–192
Mahmud R, Ramamohanarao K, Buyya R (2018) Latency-aware application module management for fog computing environments. ACM Trans Internet Technol (TOIT) 19(1):1–21
Pham X-Q, Man ND, Tri NDT, Thai NQ, Huh E-N (2017) A cost-and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. Int J Distrib Sens Netw 13(11):1550147717742073
Skarlat O, Nardelli M, Schulte S, Dustdar S (2017) Towards QoS-aware fog service placement. In: 1st IEEE international conference on fog and edge computing (ICFEC) IEEE, Madrid, Spain, pp 89–96
Wu H, Knottenbelt WJ, Wolter K (2019) An efficient application partitioning algorithm in mobile environments. IEEE Trans Parallel Distrib Syst 30(7):1464–1480
Skarlat O, Schulte S, Borkowski M, Leitner P (2016) Resource provisioning for IoT services in the fog. In: 2016 IEEE 9th international conference on service-oriented computing and applications (SOCA). IEEE, pp 32–39
Aazam M, St-Hilaire M, Lung C-H, Lambadaris I (2016) MeFoRE: QoE based resource estimation at fog to enhance QoS in IoT. In: 2016 23rd International conference on telecommunications (ICT). IEEE, pp 1–5
Agarwal S, Yadav S, Yadav AK (2016) An efficient architecture and algorithm for resource provisioning in fog computing. Int J Inf Eng Electron Bus 8(1):48
Verma S, Yadav AK, Motwani D, Raw R, Singh HK (2016) An efficient data replication and load balancing technique for fog computing environment. In: 2016 3rd International conference on computing for sustainable global development (INDIACom). IEEE, pp 2888–2895
Shekhar S, Gokhale A (2017) Dynamic resource management across cloud-edge resources for performance-sensitive applications. In: 2017 17th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID). IEEE, pp 707–710
Filip ID, Pop F, Serbanescu C, Choi C (2018) Micro services scheduling model over heterogeneous cloud-edge environments as support for IoT applications. IEEE Internet Things J 5(4):2672–2681
Taneja M, Davy A (2017) Resource aware placement of IoT application modules in fog-cloud computing paradigm. In 2017 IFIP/IEEE symposium on integrated network and service management (IM). IEEE, pp 1222–1228
Souza VBC, RamÃrez W, Masip-Bruin X, MarÃn-Tordera E, Ren G, Tashakor G (2016) Handling service allocation in combined fog-cloud scenarios. In: 2016 IEEE international conference on communications (ICC). IEEE, pp 1–5
Brogi A, Forti S (2017) QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J 4(5):1185–1192
Gill SS, Garraghan P, Buyya R (2019) ROUTER: fog enabled cloud based intelligent resource management approach for smart home IoT devices. J Syst Softw 154:125–138
Wu H, Wolter K (2017) Stochastic analysis of delayed mobile offloading in heterogeneous networks. IEEE Trans Mob Comput 17(2):461–474
Goudarzi M, Wu H, Palaniswami M, Buyya R (2020) An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Trans Mob Comput (TMC)
Pham X-Q, Huh E-N (2016) Towards task scheduling in a cloud-fog computing system. In: 2016 18th Asia-Pacific network operations and management symposium (APNOMS). IEEE, , pp 1–4
Giang NK, Blackstock M, Lea R, Leung VC (2015) Developing IoT applications in the fog: a distributed dataflow approach. In: 2015 5th International conference on the internet of things (IOT). IEEE, pp 155–162
Yousefpour A, Patil A, Ishigaki G, Kim I, Wang X, Cankaya HC, Zhang Q, Xie W, Jue JP (2019) FogPlan: a lightweight QoS-aware dynamic fog service provisioning framework. IEEE Internet Things J 6(3):5080–5096
Aazam M, Huh E-N (2015) Dynamic resource provisioning through fog micro datacentre. In: 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops). IEEE, pp 105–110
Aazam M, Huh E-N (2015) Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In: 2015 IEEE 29th international conference on advanced information networking and applications. IEEE, pp 687–694
Alsaffar AA, Pham HP, Hong C-S, Huh E-N, Aazam M (2016) An architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. Mob Inf Syst 2016
Tao Y, Wang X, Xu X, Chen Y (2017) Dynamic resource allocation algorithm for container-based service computing. In: 2017 IEEE 13th International symposium on autonomous decentralized system (ISADS). IEEE, pp 61–67
Ni L, Zhang J, Jiang C, Yan C, Yu K (2017) Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet Things J 4(5):1216–1228
Hoang D, Dang TD (2017) FBRC: optimization of task scheduling in fog-based region and cloud. In: 2017 IEEE Trustcom/BigDataSE/ICESS. IEEE, pp 1109–1114
Zhang H, Xiao Y, Bu S, Niyato D, Yu FR, Han Z (2017) Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching. IEEE Internet Things J 4(5):1204–1215
Desikan KS, Srinivasan M, Murthy CSR (2017) A novel distributed latency-aware data processing in fog computing-enabled IoT networks. In: Proceedings of the ACM workshop on distributed information processing in wireless networks, pp 1–6
Skarlat O, Nardelli M, Schulte S, Dustdar S (2017) Towards QoS-aware fog service placement. In: 2017 IEEE 1st international conference on fog and edge computing (ICFEC). IEEE, pp 89–96
Mahmud R, Srirama SN, Ramamohanarao K, Buyya R (2019) Quality of Experience (QoE)-aware placement of applications in fog computing environments. J Parallel Distrib Comput 132:190–203
Brogi A, Forti S, Guerrero C, Lera I (2019) How to place your apps in the fog-state of the art and open challenges. arXiv preprint arXiv:1901.05717
Abbasi M, Pasand EM, Khosravi MR (2020) Workload allocation in IoT-fog-cloud architecture using a multi-objective genetic algorithm. J Grid Comput 18:43–56. https://doi.org/10.1007/s10723-020-09507-1
Gupta H, Dastjerdi AV, Ghosh SK, Buyya R (2017) iFogSim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software Pract Experience 47(9):1275–1296
Zeng D, Gu L, Guo S, Cheng Z, Yu S (2016) Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans Comput 65(12):3702–3712
Lin BY, Shen H (2015) Cloud fog: towards high quality of experience in cloud gaming. In: 2015 44th International conference on parallel processing. IEEE, pp 500–509
Wang N, Varghese B, Matthaiou M, Nikolopoulos DS (2017) ENORM: a framework for edge node resource management. IEEE Trans Serv Comput
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Sriraghavendra, M., Chawla, P., Wu, H., Gill, S.S., Buyya, R. (2022). DoSP: A Deadline-Aware Dynamic Service Placement Algorithm for Workflow-Oriented IoT Applications in Fog-Cloud Computing Environments. In: Tiwari, R., Mittal, M., Goyal, L.M. (eds) Energy Conservation Solutions for Fog-Edge Computing Paradigms. Lecture Notes on Data Engineering and Communications Technologies, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-16-3448-2_2
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